Virology Analytic Pipeline

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        Download the raw data used to create the plots in this report below:

        Note that additional data was saved in multiqc_data when this report was generated.


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        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

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        Tool Citations

        Please remember to cite the tools that you use in your analysis.

        To help with this, you can download publication details of the tools mentioned in this report:

        About MultiQC

        This report was generated using MultiQC, version 1.25

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/MultiQC/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        Virology Analytic Pipeline

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        Contact Email
        Application Type
        Department

        Report generated on 2025-05-27, 22:37 UTC


        General Statistics

        Showing 0/1 rows and 3/6 columns.
        Sample NameDupsGCAvg lenMedian lenFailedSeqs
        20250414CMVpos
        81.3%
        58.0%
        700bp
        649bp
        40%
        23.1M

        Variant List

        This is the table of variants. Low quality/coverage records are omitted.

        Showing 0/7 rows and 14/14 columns.
        IndexCHROMGENEPOSREFALTAA%VarQUALEFFECTRESHGVS_P%FwdVar%RevVarCoverage
        0.0NC_006273.2UL97143177GTp.M460I2.92%766.0missense_variantYesp.Met460Ile2.53%3.31%7,321
        1.0NC_006273.2UL97143569CTp.A591V55.70%49,314.0missense_variantYesp.Ala591Val55.14%56.28%7,328
        2.0NC_006273.2UL97142883GTp.E362D32.13%47,390.0missense_variantYesp.Glu362Asp31.23%33.01%5,613
        3.0NC_006273.2UL5480415GAp.T503I23.62%14,579.0missense_variantYesp.Thr503Ile24.36%22.90%3,397
        4.0NC_006273.2UL5480359GAp.P522S72.46%49,314.0missense_variantYesp.Pro522Ser72.88%72.04%3,401
        5.0NC_006273.2UL5479120GTp.H935N8.09%825.0missense_variantNop.His935Asn7.59%8.68%1,117
        6.0NC_006273.2UL97143642GCp.M615I4.20%226.0missense_variantNop.Met615Ile4.07%4.33%7,321

        Drug Resistance

        This is the table of drug resistance. It is compiled based on the input list.

        Showing 0/5 rows and 10/10 columns.
        GENEAA%VarRESCidofovirFoscarnetGanciclovirMaribavirLetermovirReference
        0.0UL97p.M460I2.92%YesRhttps://www.eurofins-viracor.com/media/pgonfbxk/mm-0273-rev8-1121cmv-avr-mutations-references-final.pdf
        1.0UL97p.A591V55.70%YesRhttps://www.eurofins-viracor.com/media/pgonfbxk/mm-0273-rev8-1121cmv-avr-mutations-references-final.pdf
        2.0UL97p.E362D32.13%YesRhttps://www.eurofins-viracor.com/media/pgonfbxk/mm-0273-rev8-1121cmv-avr-mutations-references-final.pdf
        3.0UL54p.T503I23.62%YesRRhttps://www.eurofins-viracor.com/media/pgonfbxk/mm-0273-rev8-1121cmv-avr-mutations-references-final.pdf
        4.0UL54p.P522S72.46%YesRRhttps://www.eurofins-viracor.com/media/pgonfbxk/mm-0273-rev8-1121cmv-avr-mutations-references-final.pdf

        Variant by Functional Class

        This is the table of Variants by Functional Class.

        Showing 0/3 rows and 2/2 columns.
        TypeCountPercent
        MISSENSE22263.8%
        NONSENSE164.6%
        SILENT11031.6%

        Amplicon coverage table

        Table to show median coverage per amplicon.

        Showing 0/1 rows and 6/6 columns.
        sampleUL5434UL5412UL56UL89UL971UL972
        20250414CMVpos1,105.93,389.93,929.90.05,595.97,311.6

        FastQC

        Quality control tool for high throughput sequencing data.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        Created with MultiQC

        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        Created with MultiQC

        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        Created with MultiQC

        Sequence Length Distribution

        The distribution of fragment sizes (read lengths) found. See the FastQC help

        Created with MultiQC

        Software Versions

        Software Versions lists versions of software tools extracted from file contents.

        GroupSoftwareVersion
        FastQCfastqc0.11.9
        WorkflowNextflow24.10.6
        ph-metagenomics1.0.0
        alignmentminimap22.17-r941
        annotation_extractfieldssnpsift4.3
        bam_generationsamtools1.17
        call_annotationsnpeff5.1d
        call_deletionsmedaka1.4.4
        call_snvslofreq2.1.5
        combine_fastqfastcat0.10.2
        coveragemosdepth0.3.3
        dumpsoftwareversionspython3.11.4
        yaml6.0
        fastpfastp0.23.2
        variants_long_tablepython3.9.9
        vcf_concatbcftools1.17
        vcf_uniqvcflib1.0.3